Abstract
We develop an extreme heat validation approach for medium-range forecast models and apply it to the NCEP coupled forecast model, for which we also attempt to diagnose sources of poor forecast skill. A weighting strategy based on the Poisson function is developed to provide a seamless transition from short-term day-by-day weather forecasts to expanding time means across subseasonal timescales. The skill of heat wave forecasts over the conterminous United States is found to be rather insensitive to the choice of skill metric; however, forecast skill does display spatial patterns that vary depending on whether daily mean, minimum, or maximum temperatures are the basis of the heat wave metric. The NCEP model fails to persist heat waves as readily as is observed. This inconsistency worsens with longer forecast lead times. Land–atmosphere feedbacks appear to be a stronger factor for heat wave maintenance at southern latitudes, but the NCEP model seems to misrepresent those feedbacks, particularly over the Southwest United States, leading to poor skill in that region. The NCEP model also has unrealistically weak coupling over agricultural areas of the northern United States, but this does not seem to degrade model skill there. Overall, we find that the Poisson weighting strategy combined with a variety of deterministic and probabilistic skill metrics provides a versatile framework for validation of dynamical model heat wave forecasts at subseasonal timescales.
Highlights
The 11-year period (1999–2010) over which National Centers for Environmental Prediction (NCEP) forecasts are available is shorter than ideal; given the objectives of this study, the intent of developing a methodology that will be applied to a suite of medium-range forecast models, we argue that our focus on the NCEP model forecasts is justified
Model verification is based on four complimentary metrics: the area under the relative operating characteristic curve (AUC); reliability; the equitable threat score (ETS), and the Kullback-Leibler divergence (KLD)
Reliability and ETS both reward consistency among metric is shown in Fig. 2, where the basis of skill is the longest lead ensemble members, while penalizing false positives and false time at which model forecasts are more skillful than a negatives; reliability assess each ensemble member climatological forecast, calculated from 36 years of positive EHF
Summary
Heat waves are usually concurrent with persistent atmospheric circulation features: high pressure systems that force conditions favorable for extreme temperatures. Land surface moisture deficits and land–atmosphere feedbacks have been connected to the onset and maintenance of heat waves in many regions.[10,11,12]. Positive land–atmosphere feedbacks can exacerbate and prolong temperature anomalies, providing a form of coupled memory.[13] For forecasting purposes, land surface memory may be defined by the temporal extent of improved forecast skill when realistic land surface initiation conditions are used in a model.[14,15,16] Because land surface memory is most relevant at subseasonal timescales, accurate land surface modeling is important for subseasonal forecasts. General circulation model intercomparison projects like the Global Land-Atmosphere Coupling
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